Waveform visualization tool for facilitating medical diagnosis

ABSTRACT

A system includes a processing circuit configured to receive signals corresponding ultrasound data, extract a blood flow waveform from the signals, the blood flow waveform corresponds to a single pulse of the signals, determine a curvature characteristic of the blood flow waveform based on a plurality of local curvature parameters, and identify a medical condition for the blood flow waveform using the blood flow waveform. Each of the plurality of local curvature parameters indicates a degree to which the blood flow waveform deviates from a straight line at a location on the blood flow waveform.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a Continuation of U.S. Pat. No. 10,849,593, filedJun. 7, 2018, titled WAVEFORM VISUALIZATION TOOL FOR FACILITATINGMEDICAL DIAGNOSIS which is a Continuation of U.S. patent applicationSer. No. 15/971,260, filed May 4, 2018, which claims priority to, andthe benefit of, U.S. provisional patent application Ser. No. 62/619,015,titled WAVEFORM VISUALIZATION TOOL FOR FACILITATING MEDICAL DIAGNOSIS,and filed on Jan. 18, 2018, which are incorporated herein by referencein their entirety.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under Grant No. 1556110awarded by the National Science Foundation. The government has certainrights in the invention.

FIELD

Subject matter described herein relates generally to medical devices,and more particularly to a headset including a transducer and an outputdevice for diagnosing medical conditions.

BACKGROUND

Clinical guidelines recommend monitoring for medical conditionsincluding stroke, emboli, stenosis, vasospasm as well as elevatedintracranial pressure (ICP) which may alter cerebral blood flow. Forinstance, monitoring is performed for patients with severe traumaticbrain injury (TBI), subarachnoid hemorrhage (SAH), and other conditionswith a considerable risk of elevated ICP, because elevated ICP can leadto death or serious injury. Conventionally, a reliable method formonitoring a patient's ICP is a neurosurgeon invasively placing apressure probe into the brain tissue or cerebral ventricles. Such methodis costly, invasive, prone to infection, and is limited to in-hospitalusage. As a result, ICP monitoring is infrequently performed.

Transcranial Doppler (TCD) devices can perform non-invasive, cerebralblood flow monitoring using ultrasound which can be used for a number ofmedical conditions including those listed above. However, displays andscreens on conventional TCDs show simple waveforms without anydiagnostic visualization that can assist a physician with equipmentcalibration or diagnosis in real-time.

Acquiring the cerebral blood flow velocity (CBFV) signals using TCDrequires placement of a transducer within a specific region of the skullthin enough for the ultrasound waves to penetrate, locating a signal ofthe artery of interest, and maintaining a steady position for sufficientsample size. The location of these narrow windows varies significantlyfrom person to person. Additionally, reading and interpreting the scansonce complete is difficult because subtle features and changes in theCBFV waveforms that indicate neurological disorders are not easilydiscernible using traditional TCD analysis or visual inspection. Theserequirements make insonating (i.e., exposing to ultrasound) the desiredblood vessel difficult, thus restricting TCD use to major hospitals withexpensive, on staff expert human sonographers to operate the device aswell as reducing the overall utility of the device through utilizationof only simple analysis.

With respect to stroke detection, interventional (e.g., stentriever) andpharmaceutical (e.g., tissue plasminogen activator (tPA)) treatments forlarge vessel occlusion (LVO) need to be administered within a shortduration from symptom onset. Conventional standards for stroke diagnosisinvolves computed tomography angiography (CTA) machines, which arelimited to in-hospital uses and a small number of stroke ambulances, dueto high cost, requirement of expert operators, and intravenous (IV)injection of iodine-rich contrast material.

SUMMARY

In some arrangements, a tool for facilitating medical diagnosis includesan ultrasound device, wherein the ultrasound device is configured tocollect ultrasound data from a patient, a display device, and aprocessing circuit configured to generate a CBFV waveform based on theultrasound data, determine morphology indicators identifying attributesof the CBFV waveform, and configure the display device to display theCBFV waveform and the morphology indicators.

In some arrangements, the display device is configured to display theCBFV waveform and the morphology indicators in real time or semi-realtime as the ultrasound data is being collected.

In some arrangements, the processing circuit generates the CBFV waveformbased on the ultrasound data by generating a plurality of CBFV waveformsbased on the ultrasound data, each CBFV waveform corresponding to apulse, and the CBFV waveform used for morphology calculation is derivedfrom the plurality of CBFV waveforms.

In some arrangements, configuring the display device to display the CBFVwaveform and the morphology indicators includes configuring the displaydevice to display the plurality of CBFV waveforms in a first displaywindow.

In some arrangements, configuring the display device to display the CBFVwaveform and the morphology indicators includes configuring the displaydevice to display the CBFV waveform and the morphology indicators in asecond display window.

In some arrangements, the tool further includes deriving the CBFVwaveform from the plurality of CBFV waveforms by one or more offiltering the plurality of CBFV waveforms and averaging the plurality ofCBFV waveforms.

In some arrangements, determining the morphology indicators identifyingthe attributes of the CBFV waveform includes segmenting a pluralitydetected CBFV waveforms into distinct CBFV waveforms, and identifyingthe attributes for the CBFV waveform that is an average of the distinctCBFV waveforms.

In some arrangements, the attributes include at least one peak on theCBFV waveform.

In some arrangements, configuring the display device to display the CBFVwaveform and the morphology indicators includes configuring the displaydevice to display a peak indicator corresponding to each of the at leastone peak of the CBFV waveform.

In some arrangements, the processing circuit is further configured touse machine learning to determine that the patient is experiencing amedical condition based on the morphology indicators, and configure thedisplay device to display a notification related to the medicalcondition.

In some arrangements, in response to determining that the patient isexperiencing the medical condition, the processing circuit is furtherconfigured to send an email, a page, or a short message service (SMS)message to an operator, or call the operator.

In some arrangements, in response to determining that the patient isexperiencing the medical condition, the processing circuit furtherconfigures a medical device to administer a drug to treat the medicalcondition.

In some arrangements, the processing circuit is further configured todetermine that a probe of the ultrasound device is misaligned based onthe morphology indicators, and automatically adjust a position of theprobe.

In some arrangements, the processing circuit determines that the probeof the ultrasound device is misaligned based on machine learning.

In some arrangements, a method for facilitating medical diagnosis,includes collecting, with an ultrasound device, ultrasound data from apatient, generating a CBFV waveform based on the ultrasound data,determining morphology indicators identifying attributes of the CBFVwaveform, and displaying the CBFV waveform and the morphologyindicators.

In some arrangements, the CBFV waveform and the morphology indicatorsare displayed in real time or semi-real time as the ultrasound data isbeing collected.

In some arrangements, the CBFV waveform is generated by generating aplurality of CBFV waveforms based on the ultrasound data, each CBFVwaveform corresponding to a pulse, and deriving the CBFV waveform fromthe plurality of CBFV waveforms.

In some arrangements, displaying the CBFV waveform and the morphologyindicators includes displaying the plurality of CBFV waveforms in afirst display window, and displaying the CBFV waveform and themorphology indicators in a second display window.

In some arrangements, determining the morphology indicators identifyingthe attributes of the CBFV waveform includes segmenting a pluralitydetected CBFV waveforms into distinct CBFV waveforms, and identifyingthe attributes for the CBFV waveform that is an average of the distinctCBFV waveforms.

In some arrangements, the attributes include at least one peak on theCBFV waveform, and displaying the CBFV waveform and the morphologyindicators includes displaying a peak indicator corresponding to each ofthe at least one peak of the CBFV waveform.

In some arrangements, a non-transitory processor-readable medium storingprocessor-readable instructions such that, when executed, causes aprocessor to facilitate medical diagnosis by collecting ultrasound datafrom a patient, generating a CBFV waveform based on the ultrasound data,determining morphology indicators identifying attributes of the CBFVwaveform, and displaying the CBFV waveform and the morphologyindicators.

BRIEF DESCRIPTION OF THE FIGURES

Features, aspects, and advantages of the present invention will becomeapparent from the following description and the accompanying examplearrangements shown in the drawings, which are briefly described below.

FIG. 1 is a schematic diagram illustrating a waveform visualizationsystem according to various arrangements.

FIG. 2 is a schematic block diagram illustrating a waveformvisualization system according to various arrangements.

FIG. 3 is a processing flow diagram illustrating a method forfacilitating medical diagnosis using the waveform visualization system(FIG. 1) according to various arrangements.

FIG. 4 is a display interface showing a CBFV output diagram and a CBFVwaveform diagram of the patient (FIG. 1) according to variousarrangements.

FIG. 5A is a CBFV waveform diagram of a healthy individual according tovarious arrangements.

FIG. 5B is a CBFV waveform diagram of a patient suffering fromidiopathic intracranial hypertension (IIH) according to variousarrangements.

FIG. 5C is a CBFV waveform diagram of a healthy individual (left) and aCBFV waveform diagram of a patient suffering from LVO (right) accordingto various arrangements.

FIG. 6A is a display interface showing CBFV waveform diagrams associatedwith a left middle cerebral artery (LMCA) of a patient and CBFV waveformdiagrams associated with a right middle cerebral artery (RMCA) of thepatient according to various arrangements.

FIG. 6B is a display interface showing a CBFV waveform diagramassociated with an LMCA of a patient and a CBFV waveform diagramassociated with an RMCA of the patient superimposed on one anotheraccording to various arrangements.

FIG. 6C is a display interface showing an RMCA velocity versus an LMCAvelocity diagram associated with a patient according to variousarrangements.

FIG. 7 is a display interface showing a trending window according tovarious arrangements.

FIG. 8 is a processing flow diagram illustrating a method for extractingCBFV waveforms according to various arrangements.

FIG. 9 is a CBFV output diagram showing an example CBFV output and aslope sum function (SSF) corresponding to the CBFV output according tovarious arrangements.

FIG. 10 is a display interface showing an attribute distributionassociated with a number of CBFV waveforms according to variousarrangements.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various configurations and isnot intended to represent the only configurations in which the conceptsdescribed herein may be practiced. The detailed description includesspecific details for providing a thorough understanding of variousconcepts. However, it will be apparent to those skilled in the art thatthese concepts may be practiced without these specific details. In someinstances, well-known structures and components are shown in blockdiagram form in order to avoid obscuring such concepts.

In the following description of various arrangements, reference is madeto the accompanying drawings which form a part hereof and in which areshown, by way of illustration, specific arrangements in which thearrangements may be practiced. It is to be understood that otherarrangements may be utilized, and structural changes may be made withoutdeparting from the scope of the various arrangements disclosed in thepresent disclosure.

Arrangements described herein relate to apparatuses, systems, methods,and non-transitory computer-readable medium that provide affordable,non-invasive TCD devices in hospital and field-based (pre-hospital)settings. Such TCD devices can be used for continuously monitoring CBFV,among other parameters. As a diagnostic tool that assists a physicianwith equipment calibration (e.g., probe positioning) or diagnosis inreal-time or semi-real time, arrangements described herein include a TCDultrasound device configured to measure CBFV. The TCD ultrasound deviceis operatively coupled to a display screen configured to display visualindicators that identify the morphology of the CBFV waveforms in theCBFV output in real-time or semi-real time, to assist an operator withequipment calibration (e.g., probe positioning) and diagnosis. Sucharrangements are directed to improving TCD devices by presenting usefulmorphological information to the operator. The operator conventionallyuses his or her human judgment to determine whether a CBFV waveform as awhole appears to be problematic, without being able to identifymorphological attributes for detailed analysis in real-time orsemi-real-time.

In addition, the equipment calibration and diagnosis based on CBFVwaveform indicators can be automatically executed, in addition oralternative to displaying the visual indicators to the operator. Noconventional medical devices can perform automated equipment calibrationor diagnosis based on the CBFV waveform indicators. Thus, sucharrangements automate a process not previously automated.

Arrangements described herein relate to apparatuses, systems, methods,and non-transitory computer-readable medium that provide a standardized,quantitative, and non-invasive diagnostic tool capable of providingimproved large vessel occlusion (LVO) identification in hospital andfield-based (pre-hospital) settings. Such a diagnostic tool includes TCDdevices coupled with machine learning for rapid stroke diagnosis andallows a patient to be monitored while en route to a hospital, thusbridging a gap between incidence detection and hospital treatment.

FIG. 1 is a schematic diagram illustrating a waveform visualizationsystem 100 according to various arrangements. Referring to FIG. 1, thewaveform visualization system 100 includes at least a headset device110, a controller 130, and an output device 140.

The headset device 110 is a TCD ultrasound device configured to emit andmeasure acoustic energy in a head 102 of a patient 101. An example ofthe headset device 110 is a supine headset device. The headset device110 includes at least one probe 105 (e.g., at least one ultrasoundprobe) configured to emit and measure ultrasound acoustic energy in thehead 102. For example, the probe 105 includes at least one TCD scanner,which can automatically locate the middle cerebral artery (MCA) in somearrangements. At least one probe 105 can be positioned in a temporalwindow region (temple) of the head 102 to collect the ultrasound data.In other arrangements, the probe can be positioned over differentacoustic windows such as the transorbital window or the suboccipitalwindow. In some arrangements, headset 110 includes two ultrasound probes105, which can be placed on the temporal window region on both sides ofthe head 102. A headband, strap, Velcro®, hat, helmet, or anothersuitable wearable structure of the like connects the two probes in sucharrangements. A lubricating gel can be applied between the head 102 andthe probe 105 to improve acoustic transmission.

The controller 130 is configured to receive the ultrasound dataoutputted by the headset device 110 and to generate CBFV waveforms thatcorrespond to the ultrasound data. In that regard, the probe 110 isoperatively coupled to the controller 130 via a suitable network 120 tosend the ultrasound data to the controller 130. The network 120 can bewired or wireless (e.g., 802.11X, ZigBee, Bluetooth®, Wi-Fi, or thelike). The controller 130 can further perform signal processingfunctions to determine and display morphological indicatorscorresponding to the CBFV waveforms to facilitate a physician,clinician, technician, or care provider with diagnosis and/or to adjustthe positioning of the headset device 110 and the probe 105. Further, asdescribed, the headset device 110 can automatically adjust the positionand orientation of the probe 105 responsive to determination that theprobe 105 is not optimally placed based on the morphological indicatorsin the manner described herein. In some arrangements, the controller130, the output device 140, and a portion of the network 120 areincorporated into a single device (e.g., a touchscreen tablet device).

In some arrangements, the output device 140 includes any suitable deviceconfigured to display information, results, messages, and the like to anoperator (e.g., a physician, clinician, technician, or care provider) ofthe waveform visualization system 100. For example, the output device140 includes but is not limited to, a monitor, a touchscreen, or anyother output device configured to display the CBFV waveforms, themorphology indicators, and the like for facilitating diagnosis and/orthe positioning of the headset device 110 and the probe 105 relative tothe head 102 in the manner described.

FIG. 2 is a schematic block diagram illustrating the waveformvisualization system 100 (FIG. 1) according to various arrangements.Referring to FIGS. 1-2, the headset device 110 includes the probe 105 asdescribed. Further disclosure regarding examples of the probe 105 thatcan be used in conjunction with the waveform visualization system 100described herein can be found in non-provisional patent application Ser.No. 15/399,648, titled ROBOTIC SYSTEMS FOR CONTROL OF AN ULTRASONICPROBE, and filed on Jan. 5, 2017, which is incorporated herein byreference in its entirety. In some arrangements, the headset device 110includes manually operated probes, as opposed to automatically orrobotically-operated probes.

In some arrangements, the headset device 110 includes robotics 214configured to control positioning of the probe 105. For example, therobotics 214 are configured to translate the probe 105 along a surfaceof the head 102 and to move the probe 105 with respect to (e.g., towardand away from) the head 102 along various axes in the Cartesian,spherical, and rotational coordinate systems. In particular, therobotics 214 can include a multiple degree of freedom (DOF) TCDtransducer positioning system with motion planning. In some embodiments,the robotics 214 are capable of supporting two, three, four, five, orsix DOF movements of the probe 105 with respect to the head 102. In someinstances, the robotics 214 can translate in X and Y axes (e.g., along asurface of the head 102) to locate a temporal window region intranslational axes, and in Z axis with both force and position feedbackcontrol to both position, and maintain the appropriate force against theskull/skin to maximize signal quality by maintaining appropriate contactforce. Two angular DOF (e.g., pan and tilt) may be used to maximizenormal insonation of blood vessels to maximize velocity signals.

In some arrangements, an end of the probe 105 is operatively coupled toor otherwise interfaces with the robotics 214. The robotics 214 includecomponents, such as but not limited to a motor assembly and the like forcontrolling the positioning of the probe 105 (e.g., controlling z-axispressure, normal alignment, or the like of the probe 105). In somearrangements, the registration of the probe 105 against the head 105 isaccomplished using the robotics 214 to properly position and align theprobe 105 in the manner described.

In some arrangements, the probe 105 includes a first end and a secondend that is opposite to the first end. In some arrangements, the firstend includes a concave surface that is configured to be adjacent to orcontact a scanning surface on the head 102. The concave surface isconfigured with a particular pitch to focus generated energy towards thescanning surface. In some arrangements, the headset device 110 is a TCDapparatus such that the first end of the probe 105 is configured to beadjacent to or contact and align along a side of the head 102. The firstend of the probe 105 is configured to provide ultrasound wave emissionsfrom the first end and directed into the head 102 (e.g., toward thebrain). For example, the first end of the probe 105 can include atransducer (such as, but not limited to, an ultrasound transducer, TCD,transcranial color-coded sonography (TCCS), or acoustic ultrasoundtransducer array such as sequential arrays or phased arrays) that emitsacoustic energy capable of penetrating windows in the skull/head orneck. In other arrangements, the probe 105 is configured to emit othertypes of waves during operation, such as, but not limited to, infrared(IR), near-infrared spectroscopy (NIRS), electro-magnetic, x-rays, orthe like.

In some arrangements, the second end of the probe 105 is coupled to therobotics 214. In some arrangements, the second end of the probe 105includes a threaded section along a portion of the body of the probe105. The second end is configured to be secured in the robotics 214 viathe threads (e.g., by being screwed into the robotics 214). In otherarrangements, the probe 105 is secured in the robotics 214 by any othersuitable connecting means, such as but not limited to welding, adhesive,one or more hooks and latches, one or more separate screws, pressfittings, or the like.

The headset device 110 can further include a structural support 216configured to support the head 102 of the patient 101 and/or to supportthe headset device 110 on the head 102 or other parts of a body of thepatient 101. In some examples, the structural support 216 includes aplatform (e.g., a baseplate) that allows the patient 101 to lay down ona flat surface in a reclined or supine position while the headset device110 is operational. Further disclosure regarding such implementation ofthe structural support 216 that can be used in conjunction with thewaveform visualization system 100 described herein can be found innon-provisional patent application Ser. No. 15/853,433, titled HEADSETSYSTEM, and filed on Dec. 22, 2017, which is incorporated herein byreference in its entirety. In other examples, the structural support 216includes one or more of a mount, cradle, headband, strap, Velcro®, hat,helmet, or another suitable wearable structure of the like such that thepatient 101 can wear the headset device 110 on the head 102, shoulders,neck, and/or the like when the patient 101 is sitting, standing, orlying down. The structural support 216 can be made from any suitablymalleable material that allows for flexing, such as, but not limited to,flexible plastics, polyethylene, urethanes, polypropylene, ABS, nylon,fiber-reinforced silicones, structural foams, or the like.

While the headset device 110 is shown and described as a headset suchthat the headset device 110 is lightweight and portable, one of ordinaryskill in the art recognizes that the headset device 110 can beimplemented with other types of TCD devices.

In some arrangements, the waveform visualization system 100 includes aninput device 250. The input device 250 includes any suitable deviceconfigured to allow an operator, physician, or care provider personnelto input information or commands into the waveform visualization system100. In some arrangements, the input device 250 includes but is notlimited to, a keyboard, a keypad, a mouse, a joystick, a touchscreendisplay, or any other input device performing a similar function. Insome arrangements, the input device 250 and the output device 140 can bea same input/output device (e.g., a touchscreen display device).

In some arrangements, the network interface 260 is structured forsending and receiving data (e.g., results, instructions, requests,software or firmware updates, and the like) over a communicationnetwork. Accordingly, the network interface 260 includes any of acellular transceiver (for cellular standards), local wireless networktransceiver (for 802.11X, ZigBee, Bluetooth®, Wi-Fi, or the like), wirednetwork interface, a combination thereof (e.g., both a cellulartransceiver and a Bluetooth transceiver), and/or the like. In someexamples, the network interface 260 includes any method or deviceconfigured to send data from the headset device 110 to the controller130. In that regard, the network interface 260 may include UniversalSerial Bus (USB), FireWire, serial communication, and the like.

In some arrangements, the input device 250, the output device 140, thenetwork interface 260, and the controller 130 form a single computingsystem that resides on a same node on the network 120, and the headsetdevice 110 is connected to the computing system via the network 120, thenetwork interface 260 is configured to communicate data to and from theheadset device 110 via the network 120. In such arrangements, theheadset device 110 includes a similar network interface (not shown) tocommunicate data to and from the computing device via the network 120.In other arrangements in which the headset device 110, the controller130, the output device 140, the input device 250, and the networkinterface 260 all reside in a same computing device on a same node of anetwork, the network interface 260 is configured to communicate datawith another suitable computing system (e.g., cloud data storage, remoteserver, and the like).

In some arrangements, the controller 130 is configured for controllingoperations, processing data, executing input commands, providingresults, and the like with respect to the waveform visualization system100, and in particular, in relation to the morphology indicators asdescribed herein. For example, the controller 130 is configured toreceive input data or instructions from the input device 250 or thenetwork interface 260, to control the waveform visualization system 100to execute the commands, to receive data from the headset device 110, toprovide information (e.g., the CBFV waveforms and the morphologyindicators) to the output device 140 or network interface 260, and soon.

The controller 130 includes a processing circuit 232 having a processor234 and a memory 236. In some arrangements, the processor 234 can beimplemented as a general-purpose processor and is coupled to the memory236. The processor 234 includes any suitable data processing device,such as a microprocessor. In the alternative, the processor 234 includesany suitable electronic processor, controller, microcontroller, or statemachine. In some arrangements, the processor 234 is implemented as acombination of computing devices (e.g., a combination of a DigitalSignal Processor (DSP) and a microprocessor, a plurality ofmicroprocessors, at least one microprocessor in conjunction with a DSPcore, or any other such configuration). In some arrangements, theprocessor 234 is implemented as an Application Specific IntegratedCircuit (ASIC), one or more Field Programmable Gate Arrays (FPGAs), aDigital Signal Processor (DSP), a group of processing components, orother suitable electronic processing components.

In some arrangements, the memory 236 includes a non-transitoryprocessor-readable storage medium that stores processor-executableinstructions. In some arrangements, the memory 236 includes any suitableinternal or external device for storing software and data. Examples ofthe memory 236 include but are not limited to, Random Access Memory(RAM), Read-Only Memory (ROM), Non-Volatile RAM (NVRAM), flash memory,floppy disks, hard disks, dongles or other Recomp Sensor Board(RSB)-connected memory devices, or the like. The memory 236 can store anOperating System (OS), user application software, and/or executableinstructions. The memory 236 can also store application data, such as anarray data structure. In some arrangements, the memory 236 stores dataand/or computer code for facilitating the various processes describedherein.

As used herein, the term “circuit” can include hardware structured toexecute the functions described herein. In some arrangements, eachrespective circuit can include machine-readable media for configuringthe hardware to execute the functions described herein. The circuit canbe embodied as one or more circuitry components including, but notlimited to, processing circuitry, network interfaces, peripheraldevices, input devices, output devices, sensors, etc. In somearrangements, a circuit can take the form of one or more analogcircuits, electronic circuits (e.g., integrated circuits (IC), discretecircuits, system on a chip (SOCs) circuits, etc.), telecommunicationcircuits, hybrid circuits, and any other suitable type of circuit. Inthis regard, the circuit can include any type of component foraccomplishing or facilitating achievement of the operations describedherein. For example, a circuit as described herein can include one ormore transistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR,etc.), resistors, multiplexers, registers, capacitors, inductors,diodes, wiring, and so on.

The circuit can also include one or more processors communicativelycoupled to one or more memory or memory devices. In this regard, the oneor more processors can execute instructions stored in the memory or canexecute instructions otherwise accessible to the one or more processors.In some arrangements, the one or more processors can be embodied invarious ways. The one or more processors can be constructed in a mannersufficient to perform at least the operations described herein. In somearrangements, the one or more processors can be shared by multiplecircuits (e.g., a first circuit and a second circuit can comprise orotherwise share the same processor which, in some example arrangements,can execute instructions stored, or otherwise accessed, via differentareas of memory). Alternatively, or additionally, the one or moreprocessors can be structured to perform or otherwise execute certainoperations independent of one or more co-processors. In other examplearrangements, two or more processors can be coupled via a bus to enableindependent, parallel, pipelined, or multi-threaded instructionexecution. Each processor can be implemented as one or moregeneral-purpose processors, ASICs, FPGAs, DSPs, or other suitableelectronic data processing components structured to execute instructionsprovided by memory. The one or more processors can take the form of asingle core processor, multi-core processor (e.g., a dual coreprocessor, triple core processor, quad core processor, etc.),microprocessor, etc. In some arrangements, the one or more processorscan be external to the apparatus, for example, the one or moreprocessors can be a remote processor (e.g., a cloud-based processor).Alternatively, or additionally, the one or more processors can beinternal and/or local to the apparatus. In this regard, a given circuitor components thereof can be disposed locally (e.g., as part of a localserver, a local computing system, etc.) or remotely (e.g., as part of aremote server such as a cloud-based server). To that end, a circuit, asdescribed herein can include components that are distributed across oneor more locations.

The circuit can also include electronics for emitting and receivingacoustic energy such as a power amplifier, a receiver, a low noiseamplifier or other transmitter receiver components. In some embodiments,the electronics are an ultrasound system. In some embodiments, thesystem is comprised of a headset which is used to adjust the position ofa probe such as a TCD ultrasound probe. The headset can be configuredmanually or use an automated robotic system to position the probe over adesired location on the head. The probe transmits and receives acousticenergy which is controlled by an electronic circuit. The electroniccircuit has an analog circuit component such as a power amplifier whichsends a signal to the probe. The probe than receives the signal which isamplified by an analog low noise amplifier either within the probe or inthe analog circuit. Both the transmitted and received signals may bedigitized by the circuit. In some embodiments, the send and receivechain may be made up of entirely digital components.

An example system for implementing the overall system or portions of thearrangements can include a general-purpose computer, including aprocessing unit, a system memory, and a system bus that couples varioussystem components including the system memory to the processing unit.Each memory device can include non-transient volatile storage media,non-volatile storage media, non-transitory storage media (e.g., one ormore volatile and/or non-volatile memories), etc. In some arrangements,the non-volatile media may take the form of ROM, flash memory (e.g.,flash memory such as NAND, 3D NAND, NOR, 3D NOR, etc.), ElectricallyErasable Programmable Read-Only Memory (EEPROM), Magnetoresistive RandomAccess Memory (MRAM), magnetic storage, hard discs, optical discs, etc.In other arrangements, the volatile storage media can take the form ofRAM, Thyristor Random Access Memory (TRAM), Z-Capacitor Random AccessMemory (ZRAM), etc. Combinations of the above are also included withinthe scope of machine-readable media. In this regard, machine-executableinstructions comprise, for example, instructions and data which cause ageneral-purpose computer, special purpose computer, or special purposeprocessing machines to perform a certain function or group of functions.Each respective memory device can be operable to maintain or otherwisestore information relating to the operations performed by one or moreassociated circuits, including processor instructions and related data(e.g., database components, object code components, script components,etc.), in accordance with the example arrangements described herein.

The controller 130 further includes a signal processing circuit 238,which can be implemented with the processing circuit 232 or anotherdedicated processing circuit. The signal processing circuit 238 receivesthe ultrasound data from the headset device 110 and generates the CBFVwaveforms in the manner described. The signal processing circuit 238 canfurther determine the morphology indicators for the CBFV waveforms orthe average thereof. The signal processing circuit 238 can configure theoutput device 140 to display the CBFV waveforms, the average thereof,and the morphology indicators.

The controller 130 further includes a robotic control circuit 240, whichcan be implemented with the processing circuit 232 or another dedicatedprocessing circuit. The robotic control circuit 240 is configured tocontrol the robotics 214 based on the morphology of the CBFV waveformsduring the operation of the visualization system 100 in the mannerdescribed. In particular, the robotic control circuit 240 is configuredto control the positioning of the probe 105 using information regardingthe morphology of the waveforms.

FIG. 3 is a processing flow diagram illustrating a method 300 forfacilitating medical diagnosis using the waveform visualization system100 (FIG. 1) according to various arrangements. Referring to FIGS. 1-3,at 310, the robotics 214 can initially position the probe 105 and/or theheadset device 110 in a setup phase, before signal acquisition isperformed. The robotics 214 represent a kinematic mechanism thatpositions the transducer of the probe 105 at an acoustic window (e.g.,the temporal window region) adjacent to the head 102. The robotics 214can automatically position the probe 105 based on prior knowledge of thehuman anatomy and cerebral hemodynamics in some arrangements. In somearrangements, the robotics 214 can initially position the probe 105based on user input received by the input device 250. In somearrangements, a human operator can physically position the probe 105 atthe acoustic window.

At 320, the headset device 110 (e.g., the probe) acquires signals (e.g.,ultrasound data) during an operation phase. The ultrasound data isindicative of CBFV. The signals are streamed, via the network 120, tothe controller 130 for processing.

At 330, the signal processing circuit 238 is configured to extract CBFVwaveforms based on the signals. The streamed data can be processed andplotted (e.g., CBFV versus time) to generate a continuous CBFV output(which can be displayed in the manner described with respect to a CBFVoutput diagram 420 of FIG. 4). Extracting the CBFV waveforms refers todividing the continuous CBFV into multiple CBFV waveforms, each of whichcorresponds to a pulse or a heartbeat. Using the continuous CBFV outputas a starting point, the signal processing circuit 238 can extract theCBFV waveforms associated with the continuous CBFV output. In someexamples, extracting the CBFV waveforms can be performed using a method800 shown in FIG. 8. In another example, determining the CBFV waveformscan be performed by performing heartbeat or pulse segmentation 332 andfeature (morphology attribute) identification 334. Examples of themanner in which the signal processing circuit 238 determines the CBFVwaveforms, including heartbeat or pulse segmentation 332 and feature(morphology attribute) identification 334, can be found innon-provisional patent application Ser. No. 15/399,710, titled SYSTEMSAND METHODS FOR DETERMINING CLINICAL INDICATIONS, and filed on Jan. 5,2017, which is incorporated herein by reference in its entirety.

At 340, the signal processing circuit 238 is configured to determine aderived (e.g., average) CBFV waveform. The average CBFV waveform is anaverage (e.g., mean or median) of the CBFV waveforms in a predeterminedtime interval. For example, the average CBFV waveform may be a movingaverage or a moving mean of the CBFV waveforms in a predetermined timeinterval. The CBFV waveforms are determined per 330. The CBFV waveformsdetermined per 330 may be filtered to remove noise, before the CBFVwaveforms are averaged. One of ordinary skill in the art appreciatesthat filtering and averaging described herein are examples of how thederived or average CBFV can be derived from the CBFV waveformsdetermined per 330. The predetermined time interval can correspond tothe periodic refresh rate of the CBFV output as presented by the outputdevice 140. The predetermined time interval and/or the refresh rate ofthe CBFV output can depend on the heartrate of the patient 101, displayscreen size, processing latency/delay, user settings, and the like. Anexample of the periodic refresh rate is a periodic refresh rate of afirst window 410 of FIG. 4. The predetermined time interval can bedetermined in other suitable manners.

At 350, the signal processing circuit 238 determines morphologyindicators for the derived (e.g., average) CBFV waveform. The morphologyindicators correspond to morphological attributes of the average CBFVwaveform. Thus, determining the morphology indicators includesdetermining the morphological attributes of the average CBFV waveform.In some arrangements, given that the average CBFV waveform is an averageof the CBFV waveforms within the predetermined time interval, themorphological attributes of the average CBFV waveform can be an averageof corresponding morphological attributes of the CBFV waveforms withinthe predetermined time interval. For example, a first characteristicpeak of an average CBFV waveform may have an x-coordinate equal to anaverage (e.g., mean or median) of time values indicative of when thefirst characteristic peaks of the CBFV waveforms occur, and ay-coordinate equal to an average of CBFV values (e.g., mean or median)of the first characteristic peaks of the CBFV waveforms. In otherarrangements, the morphology attributes of the average CBFV waveform isdetermined in a manner similar to the manner in which the correspondingmorphology attributes of the CBFV waveforms within the predeterminedtime interval are determined. Alternatively, the time interval may bedetermined dynamically, for example, based on signal quality. Inparticular, the better the signal quality (e.g., high signal-to-noiseratio), the shorter the time interval needs to be.

Examples of the morphological attributes include but are not limited to,peaks, valleys, width of peaks, slopes, integrals and the like. Themorphology indicators include but are not limited to, dots, lines,highlights, arrows, boxes, brackets, texts, numbers, sounds, tactilefeedback, and the like.

At 360, the signal processing circuit 238 configures the output device140 to display the derived (e.g., average) CBFV with the morphologyindicators. Accordingly, the derived CBFV displayed by the output device140 is analyzed by the controller 130. The morphology indicators (e.g.,a dot) can identify a position of a morphological attribute (e.g., apeak) on the average CBFV waveform diagram/graph. The morphologyindicators are visual indicators that define a shape or morphology ofthe average CBFV waveform, thus visually enhancing the average CBFVwaveform by visually presenting the extracted physiological data thathave been previously ignored by care providers. In some arrangements,the signal processing circuit 238 compares the morphology indicatorswith those of a healthy individual for reference and diagnosticpurposes.

Given that the morphology of a CBFV waveform can be quite subtle, andthat the morphology can change rapidly within a short period of time, aphysician, clinician, technician, or care provider may not be able toidentify the morphology or may not have the time to do so. With themorphological indicators, the physician, clinician, technician, or careprovider can immediately understand the morphology of a CBFV waveformand the medical considerations associated therewith. Diagnosis of thepatient 101 in real-time or semi-real-time can be achieved as themorphology indicators are displayed. As such, the morphology indicatorscan assist in diagnosing and treating the patient 101 by presentinguseful information to the operator or by automatically identifyingissues corresponding to the morphology attributes.

Beyond displaying of morphology indicators, the signal processingcircuit 238 can automatically detect medical conditions or can diagnosethe patient 101 using the morphology indicators/attributes. Machinelearning can be implemented to use heuristic data of known medicalconditions and associated CBFV waveforms (or changes thereof over time)as learning examples. Based on such learning examples, morphologyattributes of interest (e.g., peaks, valleys, width of the peaks, orother defined or undefined morphology attributes) can be extracted asrepresentative criteria due to the correlation with a certain medicalcondition. Various categories can be created, including but are notlimited to, normal, medical condition type A, medical condition type B,. . . , and medical condition type N. A database (not shown) stores thecategories and the morphology indicators/attributes associatedtherewith. To identify a medical condition that the patient 101 isexperiencing, the signal processing circuit 238 can implement aclassifier to classify the average CBFV waveform, the morphologicalattributes, and/or changes thereof over time into one of the variouscategories. An example of the classifier is a kernel-based classifier,such as but not limited to a support vector machine (SVM) and spectralregression kernel discriminant analysis (SR-KDA).

The signal processing circuit 238 can configure the output device 140 toinitiate visual display, audio output, or tactile feedback to notify theoperator of the medical condition automatically detected based on themorphology indicators/attributes. The signal processing circuit 238 canconfigure the network interface 260 to send an email, a page, an SMSmessage, or call the operator to notify the operator of the detectedmedical condition. For example, the signal processing circuit 238 canconfigure the network interface 260 to notify the operator at ElectronicHealth Record (HER) interfaces, patient monitors, patient alarms, andthe like. This is can be extremely useful in a continuous monitoringscenario in which the patient 101 is continuously monitored for medicalconditions (e.g., increased ICP) and a care provider may not be presentall the time. Such automated diagnosis based on CBFV waveforms were notimplemented conventionally, nor does an operator interpret the waveformin the manner described in real-time or semi-real time. Therefore, sucharrangements improve the field of medical diagnosis by automating aprocess that is not previously automated.

Moreover, the waveform visualization system 100 can include or otherwiseoperatively coupled to other medical devices capable of actuatingmedical operations automatically based on the medical conditionsautomatically detected based on the morphology indicators/attributes.For example, responsive to determining that the patient 101 isexperiencing increased ICP, the signal processing circuit 238 canconfigure the network interface 260 to send a command to an intravenous(IV) injection machine or device to automatically administer a drug(e.g., Mannitol, Acetazolamife, and the like) of a suitable dosage totreat the increased ICP. In some examples, the dosage depends on theamount of ICP increased. The amount of ICP increased and thecorresponding dosage can also be determined based on machine learning.

In some arrangements, responsive to determining that a point on thewaveform or a difference between two points on the waveform are below orabove a threshold, ultrasound beam emitted from the probe 105 can beadjusted by the signal processing circuit 238. The adjustments caninclude but are not limited to, adjusting measurement depth, adjustingbeam power, adjusting sample size or volume, and adjusting measurementtime. For example, responsive to determining that one or more of thepeaks 510 a, 520 a, or 530 a are below a first threshold or responsiveto determining that a difference between two or more of the peaks 510 a,520 a, or 530 a are below a threshold, the signal processing circuit 238can perform one or more of increasing beam power, increasing samplesize, and increasing measurement time. On the other hand, responsive todetermining that one or more of the peaks 510 a, 520 a, or 530 a beingabove a second threshold, the signal processing circuit 238 can performone or more of decreasing beam power, decreasing sample size, ordecreasing measurement time. The first and second thresholds can bedefined using machine learning. Machine learning can be implemented touse heuristic data of known ultrasound beam characteristics (includingbut not limited to, measurement depth, beam power, sample size orvolume, and measurement time) and associated CBFV waveforms (or changesthereof over time) as learning examples. Based on such learningexamples, morphology attributes of interest (e.g., peaks, valleys, widthof the peaks, or other defined or undefined morphology attributes) canbe extracted as the first and second thresholds. A database (not shown)stores the thresholds and the morphology indicators/attributesassociated therewith.

In addition, the morphology indicators can assist in equipmentcalibration and test setup, including repositioning of the headsetdevice 110 and/or the probe 105 to improve data accuracy. By reviewingthe morphology indicators, a physician, clinician, technician, or careprovider can determine equipment misalignment or setupissues/inaccuracies.

An operator can perform actions such as but not limited to, adjusting atilt of tilt table, adjusting the probe 105 on the head 102, andapplying more gel on the head 102. In some examples, the operator canuse the input device 250 to define parameters based on which therobotics 214 can translate the probe 105 along a surface of the head 102and to move the probe 105 with respect to (e.g., toward and away from)the head 102.

Furthermore, equipment calibration or test setup can be performedautomatically using the robotic control circuit 240 and the robotics214. For instance, at 370, the signal processing circuit 238 determineswhether there is a position issue with respect to the probe 105 based onthe morphology attributes/indicators. For instance, certain morphologyattributes/indicators or changes to the morphology attributes/indicatorsover time correspond to particular misalignment of the headset device110 and/or the probe 105 with the head 102, or a lack of gel to improvetransmission. In some examples, responsive to determining that a point(e.g., the peaks 510 a, 520 a, or 530 a) on the waveform or responsiveto determining that a difference between two points (e.g., two of thepeaks 510 a, 520 a, or 530 a) on the waveform are below or above athreshold, a position issue or a lack of gel is detected.

Machine learning can be likewise implemented to use heuristic data ofknown misalignment types and associated CBFV waveforms (or changesthereof over time) as learning examples. Based on such learningexamples, morphology attributes of interest (e.g., peaks, valleys, widthof the peaks, and the like) can be extracted as representative criteriadue to the correlation with a certain type of misalignment. Variouscategories can be created, including but are not limited to, nomisalignment issue, misalignment issue type A, misalignment issue typeB, . . . , and misalignment issue type N. The categories can be definedwith respect to physical attributes of the patient 101, which includeparameters or ranges for an age, gender, weight, head size, preexistingmedical conditions, and the like. This provides further granularity indefining the categories. A database (not shown) stores the categories,the physical attributes associated therewith, and the morphologyindicators/attributes associated therewith in the form of templates. Anoperator can use the input device 250 to define the physical attributesof the patient 101. Based on those parameters or ranges, a templateassociated therewith can be retrieved and compared with the morphologyof the waveform. To determine whether a misalignment has occurred, thesignal processing circuit 238 can implement a classifier to classify theaverage CBFV waveform, the morphological attributes, and/or changesthereof over time into one of the various categories associated with thephysical attributes of the patient 101.

Responsive to determining that there are no position issues (370:NO),the method 300 ends. On the other hand, responsive to determining thatthere is a position issue (370:YES), the robotic control circuit 240configures the robotics 214 to reposition the probe 105 based on themorphology indicators/attributes, at 380.

In some arrangements, either displaying the morphology indicators (360)or automatically adjusting the probe 105 (370 and 380) is performed. Inother arrangements, both displaying the morphology indicators andautomatically adjusting the probe 105 are performed in any suitablesequence or simultaneously.

FIG. 4 is a display interface 400 showing a CBFV output diagram 420 anda CBFV waveform diagram 440 of the patient 101 (FIG. 1) according to oneexample. Referring to FIGS. 1-4, the display interface 400 is an exampleof an interface displayed by the output device 140 at 360. The outputdevice 140 displays the CBFV output diagram 420 in a first window 410 ofthe display interface 400. The output device 140 displays the CBFVwaveform diagram 440 in a second window 430 of the display interface400. The second window 430 can be referred to as a morphology displaywindow. The vertical axes in the diagrams 420 and 440 correspond toblood flow velocity (in cm/s or cm/ms), and the horizontal axes in thediagrams 420 and 440 correspond to time (in s or ms). The CBFV outputdiagram 420 can be displayed in real-time or semi-real time as thesignals (e.g., ultrasound data) are collected at 320. Displaying of theCBFV output diagram 420 may be delayed due to signal processing. Somemethods of heartbeat or pulse segmentation 332 and feature (morphologyattribute) identification 334 may not be used in real-time withstreaming data as future knowledge of the signals are needed for moreaccurate processing. Accordingly, in some arrangements, the controller130 introduces a reporting latency. The CBFV output diagram 420 iscontinuously updated or periodically updated as new signals arecollected at 320.

As shown, the CBFV output diagram 420 visually presents multiplecontinuous CBFV waveforms for a given time interval as determined at330. The CBFV output is pulsatile, driven by the cardiac cycle of thepatient 101. The CBFV output appears to be periodic in nature, with eachdistinct CBFV waveform (each period) corresponding to a pulse orheartbeat. The CBFV waveforms shown in the diagram 420 appear to havemorphological features such as but not limited to peaks and valleys.However, given the irregularities of the CBFV output and that the CBFVoutput diagram 420 is constantly updated to account for new data, it isdifficult to diagnose based on the CBFV output diagram 420 withoutassistance from visual indicators that visually identify and emphasizethe morphological features to allow an operator to perceive what theCBFV waveforms mean immediately.

The CBFV waveform diagram 440 displays the derived (e.g., average) CBFVwaveform determined at 340. The average CBFV waveform is the average ofthe multiple waveforms displayed in the CBFV output diagram 420. Bydisplaying an average CBFV waveform, negative effects, such as but notlimited to, noise and fluctuation in the raw signals acquired at 320 canbe reduced. The CBFV waveform diagram 440 can also display an averageCBFV waveform that has been graphically processed (such as but notlimited to, smoothed, enlarged, and scaled) to emphasize certainmorphology features. In other arrangements, the CBFV waveform diagram440 displays a waveform selected by the signal processing circuit 238from multiple waveforms captured for the predetermined period of time.

In some arrangements, the CBFV waveform diagram 440 displays the derived(e.g., average) CBFV waveform with at least one previous average CBFVwaveform, all superimposed on each other in a same diagram or displayedadjacent to each other to illustrate changes of the average CBFVwaveforms over time. In an example in which the CBFV output diagram 420is updated periodically such that an average CBFV waveform is determinedfor each period, each of the at least one previous average CBFV waveformcorresponds to a previous period that is no longer displayed.

In some arrangements, the CBFV waveform diagram 440 displays the derived(e.g., average) CBFV waveform with at least one of the CBFV waveformsdisplayed in the CBFV output diagram 420, superimposed on each other ina same diagram or displayed adjacent to each other. In somearrangements, the CBFV waveform diagram 440 displays two or more CBFVwaveforms displayed in the CBFV output diagram 420 (without displayingthe derived CBFV), all superimposed on each other in a same diagram ordisplayed adjacent to each other. Aligning any CBFV waveforms can beachieve due to beat segmentation, which identifies a starting point andan end point of a particular CBFV waveform.

In the arrangements in which the CBFV waveform diagram 440 displaysmultiple CBFV waveforms, the morphology indicators for one of the CBFVwaveforms are displayed to avoid visual crowding and confusion. In otherarrangements, the morphology indicators for two or more of the CBFVwaveforms are displayed.

FIGS. 5A and 5B show a non-limiting example of a manner in whichmorphology or changes in morphology as evidenced by the morphologyindicators can be used to detect medical conditions, such as increasedICP. FIG. 5A is a CBFV waveform diagram 500 a of a healthy individualaccording to one example. FIG. 5B is a CBFV waveform diagram 500 b of apatient (of comparable physical characteristics such as gender, age, andrace) suffering from idiopathic intracranial hypertension (IIH)according to one example. Referring to FIGS. 1-5B, the vertical axes inthe diagrams 500 a and 500 b correspond to blood flow velocity (in cm/sor cm/ms), and the horizontal axes in the diagrams 500 a and 500 bcorrespond to time (in s or ms). The diagrams 500 a and 500 b may or maynot be displayed with an underlying CBFV output diagram (e.g., the CBFVoutput diagram 420). The CBFV waveforms shown in the diagrams 500 a and500 b can be derived from (e.g., filtered from, an average (mean ormedian) of, and the like) the underlying CBFV output for a predeterminedtime interval in the manner described. Alternatively, the CBFV waveformsshown in the diagrams 500 a and 500 b can be selected by the signalprocessing circuit 238 from multiple waveforms captured for thepredetermined time interval.

The CBFV waveform diagrams 500 a and 500 b, including morphologyindicators 510 a-530 a and 510 b-530 b, can be displayed by the outputdevice 140 to assist a physician, clinician, technician, or careprovider with diagnosis, in some arrangement for increased or high ICP.FIGS. 5A and 5B show a case where traditional CBFV metrics such meanvelocity, systolic velocity, and diastolic velocity with respect to theCBFV waveform diagrams 500 a and 500 b are equal. As such, thetraditional CBFV metrics do not provide insight for diagnosis, however,the morphological indicators might.

In the CBFV waveform diagram 500 a, a second characteristic peak(visually identified by the morphology indicator 520 a) is a firstdistance away from a first characteristic peak (visually identified bythe morphology indicator 510 a). In the CBFV waveform diagram 500 b, asecond characteristic peak (visually identified by the morphologyindicator 520 b) is a second distance away from a first characteristicpeak (visually identified by the morphology indicator 510 b). The seconddistance is considerably shorter than the first distance. The distancebetween the first characteristic peak and the second characteristic peakcan be used to determine increased or high ICP, given that the distancebetween the first characteristic peak and the second characteristic peakcan correlate with ICP. Specifically, shorter distance between the firstcharacteristic peak and the second characteristic peak is typicallyassociated with higher ICP.

As such, by displaying the morphology indicators 510 a-530 a and 510b-530 b, a physician, clinician, technician, or care provider canimmediately perceive the relationships between morphology of the CBFVwaveforms shown in the diagrams 500 a and 500 b in real-time, as such,measurements are taking place, to diagnose the patient and to takeactions. In some arrangements, these measurements are from two differentpeople at two different times. It may be possible to used stored,normative data of that range and compare it. It also may be possible tocompare waveforms from two sides. Or, it may be possible to compare tostored waveforms of that subject. To further notify an operator of themorphology of the CBFV waveforms shown in the diagrams 500 a and 500 b,additional morphology indicators 540 a and 540 b can be used to visuallyemphasis the first distance and the second distance, respectively. Otherforms of visual or audio notifications, warnings, or tactile feedbackcan be provided if the distance between the first characteristic peakand the second characteristic peak falls below a predeterminedthreshold. The predetermined threshold can be an absolute length (e.g.,in cm) or a percentage (e.g., a 5%, 10%, 15%, 20%, or the like of theblood flow velocity of the first characteristic peak or of the secondcharacteristic peak). In other examples, the predetermined thresholdcorresponds to the value of the second characteristic peak exceeding thevalue of the first characteristic peak.

FIGS. 5A and 5B show an exemplary connection between specific CBFVwaveform morphology attributes and ICP. One of ordinary skill in the artcan appreciate that other connections between CBFV waveform morphologyattributes and other medical conditions exist and can be likewisevisually presented (e.g., identified or highlighted by morphologyindicators) to assist a physician, clinician, technician, or careprovider with diagnosis of those medical conditions. To name a few, CBFVwaveform morphology attributes are linked to vasodilatation,vasoconstriction, capillary bed expansion, and the like.

While FIGS. 4-5B show morphology indicators 450-470, 510 a-530 a, and510 b-530 b that correspond to peaks, one of ordinary skill in the artcan appreciate that morphology indicators corresponding to othermorphological features (e.g., valleys, slopes at peaks, slopes atvalleys, width of a peak, and the like) of the CBFV waveforms can belikewise displayed. Such morphology indicators/attributes can belikewise implemented for machine learning.

With respect to stroke analysis, a trained operator typically examinesdampened signal, blunted signal, minimal signal, or absent signal of aCBFV waveform to detect stroke. This relies on an operator's skill andinterpretation, which is subjective. The dampened signal, bluntedsignal, minimal signal, and absent signal also correspond to an overallfeel of the CBFV waveform and does not relate to particularmorphological attributes. Arrangements disclosed herein relate tographically presenting the morphological attributes using suitableindicators to assist an operator in detecting and analyzing stroke.Additional arrangements allow automated detection of stroke, a CBFVwaveform-based process that had not been previously automated.

FIG. 5C is a CBFV waveform diagram 500 c of a healthy individual (left)and a CBFV waveform diagram 500 d of a patient suffering from LVO(right) according to one example. FIG. 5C shows non-limiting examples ofa manner in which morphology or changes in morphology as evidenced bythe morphology indicators can be used to detect medical conditions, suchas LVO. Referring to FIGS. 1-5C, the vertical axes in the diagrams 500 cand 500 d correspond to blood flow velocity (in cm/s or cm/ms), and thehorizontal axes in the diagrams 500 c and 500 d correspond to time (in sor ms). The diagrams 500 c and 500 d may be displayed by the outputdevice 140. The diagrams 500 c and 500 d may be displayed with anunderlying CBFV output diagram (e.g., the CBFV output diagram 420). TheCBFV waveforms shown in the diagrams 500 c and 500 d can be derived from(e.g., filtered from, an average (mean or median) of, and the like) theunderlying CBFV output for a predetermined time interval in the mannerdescribed. Alternatively, the CBFV waveforms shown in the diagrams 500 cand 500 d can be selected by the signal processing circuit 238 frommultiple waveforms captured for the predetermined time interval.

Curvature of a CBFV waveform can be used to diagnose LVO. Curvature is arobust metric for assessing the presence of LVO, conferring variousadvantages over traditional heuristic procedures. Traditional heuristicprocedures require acquisition of CBFV waveforms and power m-mode (PMD)waveforms from multiple vessels in each hemisphere, thus requiringhighly trained personnel with advanced anatomical knowledge for dataacquisition and analysis. On the other hand, arrangements disclosedherein utilize curvature, which possesses powerful predictive utilityeven as measured from a single brief recording of MCA flow. This can besignificantly enhanced by a paired bilateral recording, regardless ofinter-hemispheric depth disparity, and occlusion location. Thearrangements can be performed in real-time. The displaying of themorphology indicators (e.g., colors, highlights, pointers,notifications, warnings, and the like) can be easily understood andcommunicated in real-time by care providers with minimal training.

First, curvature for each waveform can be determined in suitablemanners. In a non-limiting example, for an exemplary waveform denotedx(t), below, local curvature (k(t)) can be computed at each time point(t) via the following expression:

$\begin{matrix}{{k(t)} = \frac{{x^{''}(t)}}{\left( {1 + {{x^{\prime}}^{2}(t)}} \right)^{\frac{3}{2}}}} & (1)\end{matrix}$

The signal processing circuit 238 can determine a single curvaturemetric for each waveform by summing local curvature (e.g., determinedusing expression (1)) over all time points, including time pointsassociated with a beat “canopy.” The beat canopy is defined as a set oftime points corresponding to velocities that exceed a given threshold(e.g., 25%) of a total diastolic-systolic range of the waveform. Inother words, the beat canopy refers to all time points (t) such that:

${{x(t)}{{x\left( t_{d} \right)} + \frac{{x\left( t_{s} \right)} - {x\left( t_{d} \right)}}{4}}},$

where t_(d) and t_(s) represent time points corresponding to a diastolicminimum and a systolic maximum, respectively.

Next, the curvature for each waveform can be graphically presented viathe output device 140 using suitable morphology indicators to enablereal-time observation and decision-making by care providers. Curvatureis a subtle morphology feature often not distinguishable by an operator,especially when the diagrams 500 c and 500 d are presented in real-timeand updated frequently. In the non-limiting example shown in diagrams500 c and 500 d, areas of relatively high curvature are denoted withcircles while areas with relatively low curvatures are denoted withtriangles. As shown, the diagram 500 c of a healthy individual showshigh curvature, at or approximately close to peaks. On the other hand,the diagram 500 d of a patient with LVO exhibits low curvature, even atthe peaks.

Machine learning can be implemented to use heuristic data of knownmedical conditions and associated curvature of CBFV waveforms (orchanges of the curvature over time) as learning examples. Based on suchlearning examples, curvature and associated locations of the curvaturecan be extracted as representative criteria due to the correlation witha certain medical condition. Various categories can be created,including but are not limited to, normal, medical condition type A,medical condition type B, . . . , and medical condition type N. Adatabase (not shown) stores the categories and the curvature informationassociated therewith. To identify a medical condition that the patient101 is experiencing, the signal processing circuit 238 can implement aclassifier to classify the curvature information of the CBFV waveformand/or changes thereof over time into one of the various categories.

FIG. 6A is a display interface 600 a showing CBFV waveform diagrams 610a-660 a associated with an LMCA of a patient and CBFV waveform diagrams610 b-660 b associated with an RMCA of a patient according to oneexample. Referring to FIGS. 1-6A, the display interface 600 a can bedisplayed by the output device 140. Each of the waveform in the diagrams610 a-660 a and 610 b-660 b can be derived from (e.g., filtered from, anaverage (mean or median) of, and the like) multiple waveforms in acontinuous CBFV output for a predetermined period of time. Each row ofdiagrams correspond to a particular depth (e.g., 50 mm, 52 mm, . . . ,60 mm) at which the signals are gathered by the probe 105. Thus, foreach depth, a CBFV waveform diagram associated with LMCA and anotherCBFV waveform diagram associated with RMCA are displayed adjacent to oneanother to allow juxtaposition of similar diagrams. This allows anoperator to clearly see the differences between LMCA and RMCA at aparticular depth.

The differences can be used to diagnose stroke. In some examples,consistent and significant differences in curvature across the differentdepths between LMCA and RMCA can be used as an indication of LVO.Consistency can be evaluated on a threshold basis. For example,significant differences above a set threshold number (e.g., 50%, 60%,75%, and the like) of the depths measured correlates with actual LVO. Inthe display interface 600 a, the left likely has LVO given that withrespect to all of the depths measured, the LMCA is associated with alesser degree of curvature as compared to that of corresponding pointsor peaks on the RMCA. In some examples, progressive differences betweenwaveforms (e.g., differences between peak values) can be used todetermine a depth at which LVO occurs. As shown in the display interface600a, the difference between corresponding peaks in LMCA and RMCA ismost pronounced at 50 mm. This indicates that the LVO is likelyoccurring at 50 mm. The morphologies (e.g., curvature and peak values)between LMCA and RMCA diverge the greatest at 50 mm as compared to otherdepths.

FIG. 6B is a display interface 600 b showing a CBFV waveform diagram 610c associated with an LMCA of a patient and a CBFV waveform diagram 610 dassociated with an RMCA of the patient superimposed on one anotheraccording to one example. Referring to FIGS. 1-6B, the display interface600 b can be displayed by the output device 140. Each of the waveformsin the diagrams 610 c and 610 d can be an average (e.g., mean or median)of multiple waveforms in a continuous CBFV output for a predeterminedperiod of time. Instead of displaying the diagrams side-by-side similarto the display interface 600 a, the display interface 600 b displays thediagrams 610 c and 610 d in a same diagram, being superimposed on oneanother. Additional graphical indicators such as colors, arrows, text,and the like can be implemented to distinguish the two plots. Forexample, the diagrams 610 c and 610 d can be shown in different colors.

In some arrangements, morphological indicators such as those describedherein can be added to the diagrams 610 a-660 a, 610 b-660 b, 610 c, and610 d.

FIG. 6C is a display interface 600 c showing an RMCA velocity versusLMCA velocity diagram 610 e associated with a patient according to oneexample. Referring to FIGS. 1-6C, the display interface 600 c can bedisplayed by the output device 140. The display interface 600 c can beanother display interface to organize the underlying data of theinterface 600 a. Each dot on the diagram 610 e represents the RMCAvelocity versus the LMCA velocity for a particular depth. The depth canbe differentiated by different colors or other visual distinctions suchas shapes of the dots. At least one extrapolation line can be used toshow trend.

While FIGS. 6A-6C are concerned with comparing RMCA and LMCA, one ofordinary skill in the art can appreciate that the interfaces 600 a-600 ccan be similarly used to juxtapose any two comparable CBFV waveforms,such as one from a healthy individual (control group) with another froma patient with a disease or suspected to have a disease. The two CBFVwaveforms can be displayed side-by-side based on depths (similar tointerface 600 a), superimposed (similar to interface 600 b), or have theassociated velocities plotted against each other (similar to interface600 c).

FIG. 7 is a display interface showing a trending window 700 according toone example. Referring to FIGS. 1-7, the trending window 700 can bedisplayed with one or more other interfaces described herein to provideadditional information to assist a physician, clinician, technician, orcare provider with diagnosis and/or to adjust the positioning of theheadset device 110 and the probe 105. The trending window 700 can beused to trend various parameters related to the CBFV waveforms includingbut are not limited to, curvature, CBFV, transformations of the CBFV(e.g., those used to emphasize the upslope of a segmented CBFVwaveform), SSF of the CBFV, and the like. In particular, the trendingwindow 700 trends a parameter 710. The trending window 700 can includelimits 720 a and 720 b for visual assistance of tracking the parameter710.

FIG. 8 is a processing flow diagram illustrating a method 800 forextracting CBFV waveforms according to one example. Referring to FIGS.1-8, the method 800 can be implemented to extract individual pulses andthe CBFV waveforms associated thereof from the continuous signals(continuous CBFV output) acquired at 320. The extracted waveforms can beused as visual diagnosis aides to an operator and/or can be used toadjust positions/orientations of the probe 105 in the manner described.Thus, the CBFV waveform analysis as described herein depends on reliablepulse onset detection. A pulse onset defines a beginning of a pulse or aheartbeat. Accurate CBFV waveform extraction presents a significantchallenge for a number of reasons. For one, TCD measurements areaffected by signal attenuation due to the skull, thus resulting in arelatively low signal-to-noise ratio. TCD is highly operator-dependentand relies on the operator's ability to locate the acoustic window andto insonate the appropriate vessel within a cerebrovasculature, whichvaries among individual patients. Additionally, the CBFV signals areparticularly prone to noise artifacts as a result of motion of the probe105 and/or the patient 101. Furthermore, a large variety of possiblewaveform morphologies can further make CBFV waveform extractiondifficult due to lack of predictability. The method 800 addresses suchtechnical issues. In some arrangements, in the absence of any TCDdevices or in conjunction with TCD devices, beat start and stop pointscan be identified using at least another physiological parameter of theheart including but not limited to, Electrocardiogram (EKG), pulseoximetry, heartrate monitors, and the like.

At 810, the signal processing circuit 238 applies a band-pass filter tothe signals acquired at 320. In some examples, the band-pass filter isconfigured to filter out signals outside of a desired range to filterout noise. Examples of the designed range include but are not limitedto, 0.5-10 Hz.

At 820, the signal processing circuit 238 enhances at least one sharpupslope that can define a start of a CBFV waveform. In one arrangement,enhancement of the sharp upslope can be achieved by applying a windowedslope sum function (SSF) to the filtered signals generated as a resultof 810. The windowed SSF effectively measures a net change in thecontinuous CBFV output shown in graph 900 a over a time interval. Anon-limiting example of the SSF (Z_(i)) is:

$\begin{matrix}{{z_{i} = {{\sum\limits_{k = {i - w}}^{i}y_{k}} - y_{k - 1}}}\;} & (2)\end{matrix}$

where w is a length of an analyzing window. In addition, y_(k) andy_(k−1) are adjacent filtered CBFV output signals. In some examples, alength of the analyzing window is equal to, approximately equal to,slightly less than a length of an initial upslope of a typical pulse.Examples of the length of the analyzing window include but are notlimited to, 100 ms, 110 ms, 120 ms, 125 ms, 130 ms, and 145 ms. In otherarrangements, a difference between a highest point and a lowest point ofthe CBFV waveform is the net change.

FIG. 9 is a CBFV output diagram showing an exemplary CBFV output 900 aand a SSF signals 900 b corresponding to the CBFV output 900 a accordingto one example. Referring to FIGS. 1-9, the CBFV output 900 a and theSSF signals 900 b are presented in normalized graphs. The CBFV output900 a and the SSF signals 900 b are time-aligned. The SSF signals 900 bshows the SSF signals corresponding to the signals shown in the CBFVoutput 900 a. The SSF signals 900 b can be determined from the signalsof the CBFV output 900 a using the expression (2) or another suitablemethod.

At 830, the signal processing circuit 238 determines window locationsbased on the SSF signals. The window locations define windows in which apulse onset is likely to occur. To achieve this, the signal processingcircuit 238 determines thresholds for the SSF signals. In some examples,the threshold can be established at 60% of an average (mean or median)of a predetermined number (e.g., 10 or a number of peaks identified ifthe number is less than the predetermined number) of preceding peaks inthe SSF signals. A peak is defined as a maximum value of an upslope of aCBFV pulse.

At an initialization phase in which no preceding peaks can be used toestablish a threshold, all peaks exceeding a peak threshold areidentified by the signal processing circuit 238. An example of the peakthreshold is 3 times the average (mean or median) of the SSF signalsover a first 10 seconds of the data acquired at 320. The signalprocessing circuit 238 can set an initial threshold at 60% of an average(mean or median) value of the identified peaks. Responsive to theinitial threshold being determined, a threshold line 950 is generated tobe horizontally transverse the SSF signals of the CBFV output diagram900 a. Threshold crossing points 910 b-940 b are points on the diagram900 b that intersect with the threshold line 950. Vertical lines can begenerated at the threshold crossing points 910 b-940 b to be verticallytransverse to the diagrams 900 a and 900 b. A search window is definedas a time interval between a threshold crossing point (e.g., 920 b) anda peak (e.g., 920 a) of a last-detected pulse immediately preceding anew search window. The new search window can be defined in a mannersimilar to disclosed with the search window. For a very first onset, thesearch window is defined as a time interval between a very firstthreshold crossing point and a beginning of the SSF signals.

In order to avoid locating multiple threshold crossing pointsimmediately adjacent to one another, a refractory period is enforced bythe signal processing circuit 238. Within the short refractory period,the signal processing circuit 238 refrains from defining new thresholdcrossing points. Exemplary lengths of the refractory period include butare not limited to, 150 ms. One of ordinary skill in the art canappreciate that other suitable lengths of the refractory period can belikewise implemented, as long as the refractory period is longer than apulse upslope time and significantly shorter than an entire pulselength.

The peaks 920 a, 940 a, 960 a, and 980 a of each beat should occur closeto the threshold crossing points 910 b, 920 b, 930 b and 940 b,respectively. In some arrangements, the peaks 920 a, 940 a, 960 a, and980 a are determined by locating a maximum value that occurs within apredetermined time interval (such as but not limited to, about 150 ms)of the corresponding threshold crossing points 910 a, 920 b, 930 b and940 b, respectively. In some arrangements, peak finding can occur asseparately from onset locating, responsive to all onsets being located.

At 840, the signal processing circuit 238 performs onset identification.Responsive to a search window being identified, valleys (e.g., 910 a,930 a, 950 a, and 970 a) in the original filtered signals (shown in thediagram 900 a) that occur within the search window are identified. Avalley that is both closest to a threshold crossing point and satisfiesa condition such as but not limited to,CBFV_(peak)−CBFV_(valley)≥A(SSF_(peak)) is designated as a pulse onset.CBFV_(peak) is a peak value of a CBFV pulse. CBFV_(valley) is the valueof the candidate valley. SSF_(peak) is a peak value of the SSF signalsfor this search window. Factor A is included to avoid falling intovalleys that appear in the upslope due to noise artifacts orpathological morphologies. Examples of factor A include but are notlimited to, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, andabout 0.5-0.9. Examples of the onsets as shown in diagram 900 b includethe valleys 910 a, 930 a, 950 a, and 970 a. As such, initial estimatesfor the waveform onsets are accordingly determined.

At 850, the signal processing circuit 238 analyzes beat length toaddress outliners. After the output 900 a has been scanned in itsentirety, and the initial onsets are determined per 840, the outlinersare addressed based on beat length. The initial processes 810-840 mayresult in two mistakes, “long beats” and “short beats.” Long beatstypically occur when a beat is missed, resulting in two beats detectedas a single beat. This result may be due to some abnormality in theupslope of the beat, either because the upslope is not sufficientlysteep and fails to cross the threshold line (e.g., 950) or because theupslope contains some noise artifacts that suppress the SSF signals.Short beats typically occur as noise causes a sharp upslope based onwhich a new beat is detected, thus dividing what should be a single beatinto two or more shorter beats.

In a non-limiting example, beats are determined to be outliers using alength-based median absolute deviation (MAD) method. For each point inthe SSF signals 900 b, MAD can be computed using the followingexpression:

MAD_(i)=median(|X_(i)−median(X)|)   (3)

where X is a univariate data set of the SSF signals 900 b, havingelements X_(i). Mad can be converted into a proxy for standard deviationby including a scale factor, such as:

{circumflex over (σ)}=B(MAD)   (4)

where an example of B is about 1.4826. One of ordinary skill in the artcan appreciate that other suitable examples of the scale factor B andoutliner detection mechanism can be likewise implemented.

In some arrangements, short beats can be defined as beats with a lengthl that satisfies a condition l<l_(median)−C({circumflex over(σ)}_(length)). In some arrangements, long beats can be defined as beatswith a length l that satisfies a condition l<l_(median)+C({circumflexover (σ)}_(length)). C is a constant such as but not limited to, about3.5. C can be any suitable conservative criterion for classifyingoutliers.

In some arrangements, the signal processing circuit 238 can address thelong beats before the short beats. Global beat detection in the mannerdescribed with respect to 830-840 can be applied on a smaller scale toaddress the long beats, with progressively relaxed thresholds. First, asearch window is defined with respect to the CBFV signals from thebeginning of a peak of an identified long beat to the end of the longbeat. The SSF is determined for this segment of CBFV signals. Athreshold is set at 60% of the average (mean or median) of all the peakslocated in the original global SSF signals during a first pass (e.g.,810-840). The original global SSF signals include the SSF correspondingto the long beat, regular beats, and outer outliner long or short beats.The window locations and onset locations are determined in a same manneras disclosed with respect to 830 and 840, for the SSF signalscorresponding to the long beat using such threshold. If new onsets arelocated, those onset locations are saved. The method proceeds to a nextlong beat, if any. If no new onsets are located, the threshold(initially at 60%) is incrementally relaxed (decreased). The onsetdetection is repeated with each iteration associated with relaxedthreshold until new onsets are located. For example, for a nextiteration, the threshold is set at an increment (e.g., 5%) less than theprevious threshold. If no new onsets are found after reducing thethreshold value to an increment before the threshold value reaches 0,the long beat is left alone.

Short beats are dealt with after all the long beats have been addressedin some arrangements. The short beats are addressed by viewing eachshort beat along with its immediate adjacent neighbors to determinewhether the short beat should be combined with either of its neighboringbeats. If a merger of the short beat with a neighbor beat results in anew beat with a length closer to the average beat length than theoriginal beats, then the merger is performed. In an exemplaryarrangement, four lengths related to a short beat are determined:l_(before), l_(short), l_(after), and l_(median). In some examples,l_(before) defines a length of a beat adjacent to and before the shortbeat. l_(short) defines a length of the short beat itself l_(after)defines a length of the beat adjacent to and after the short beat.l_(median) is an average (mean or median) beat length of all beats thathave been found in the CBFV signals, including beats other than theshort beat and its neighbors. A length of a beat is defined to be a timeinterval between consecutive onsets. The signal processing circuit 238first checks whether combining l_(short) with l_(after) produces a newbeat with a length closer to l_(median) than l_(after). Responsive todetermining that the length of the new beat is closer to l_(median),then the beats are combined responsive to determining that a correlationdistance between the beats is greater than a threshold, such as but notlimited to about 0.1. This is because merging beats involves deleting abeat onset, which should be handled very conservatively. After mergingthe beats, the method proceeds to a next short beat. If combiningl_(short) with l_(after) fails to produce a new beat with a lengthcloser to l_(median) than l_(after) or if the correlation distancebetween the beats is not greater than the threshold, then the signalprocessing circuit 238 checks whether combining l_(short) withl_(before) produces a new beat with a length closer to l_(median) thanl_(before). Responsive to determining that the length of the new beat iscloser to l_(median), and that a correlation distance between the beatsis greater than the threshold, the beats are combined. This algorithmcan be performed for all short beats until no short beats are remaining.

A single pass often may not address all long and short beats due to thefact that as the beats are added and/or subtracted, statistics (e.g.,average peak, l_(median), and the like) may change. Thus, the beatlength analysis at 850 ends responsive to determining that no new beatsare added and/or subtracted during a single iteration. In someinstances, an oscillating solution may be reached, such that a maximumnumber of iterations (e.g., 10) should be enforced to avoid theping-pong effect of shifting statistics.

In some arrangements, actionable information can be extracted from adistribution of certain attributes of CBFV waveforms. Examples of suchattributes include but are not limited to, an average velocity, skew,curvature, kurtosis, and the like of each waveform or of a given peak(e.g., a first peak) of each waveform. Such information can bedetermined by the controller 130 and displayed on an interface providedby the output device 140. FIG. 10 is a display interface 1000 showing adiagram of an attribute distribution associated with a number of CBFVwaveforms according to various arrangements. As shown, an x-axis of thediagram corresponds to an attribute (e.g., curvature) of a first peak ofeach waveform. A y-axis of the diagram corresponds to a number ofoccurrences of a particular attribute value (e.g., 2.5, 5, 7.5, 10,12.5, and the like) among the number of CBFV waveforms. For example, 1CBFV waveform has a curvature of approximately 2.5, 2 CBFV waveformshave a curvature of approximately 5, 4 CBFV waveforms have a curvatureof approximately 7.5, and 3 CBFV waveforms have a curvature ofapproximately 10.

While curvature is used as a non-limiting example, one of ordinary skillin the art can appreciate other the distribution of other attributes canbe similarly graphed. For instance, the x-axis of the graph definevalues of the attribute while the y-axis of the graph define occurrencesof that attribute among the CBFV waveforms or among peaks (e.g., firstpeaks) of the CBFV waveforms. In addition, the output device 140 cansimilarly display a distribution of a certain attribute of a givensubject being compared (e.g., overlaid) with the distributions of thesame attribute of other subjects or with an average distribution acrossa population (e.g., a general population, a segmented population, andthe like).

The above used terms, including “held fast,” “mount,” “attached,”“coupled,” “affixed,” “connected,” “secured,” and the like are usedinterchangeably. In addition, while certain arrangements have beendescribed to include a first element as being “coupled” (or “attached,”“connected,” “fastened,” etc.) to a second element, the first elementmay be directly coupled to the second element or may be indirectlycoupled to the second element via a third element.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but is to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. All structural andfunctional equivalents to the elements of the various aspects describedthroughout the previous description that are known or later come to beknown to those of ordinary skill in the art are expressly incorporatedherein by reference and are intended to be encompassed by the claims.Moreover, nothing disclosed herein is intended to be dedicated to thepublic regardless of whether such disclosure is explicitly recited inthe claims. No claim element is to be construed as a means plus functionunless the element is expressly recited using the phrase “means for.”

It is understood that the specific order or hierarchy of steps in theprocesses disclosed is an example of illustrative approaches. Based upondesign preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged while remainingwithin the scope of the previous description. The accompanying methodclaims present elements of the various steps in a sample order, and arenot meant to be limited to the specific order or hierarchy presented.

The previous description of the disclosed implementations is provided toenable any person skilled in the art to make or use the disclosedsubject matter. Various modifications to these implementations will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other implementations without departingfrom the spirit or scope of the previous description. Thus, the previousdescription is not intended to be limited to the implementations shownherein but is to be accorded the widest scope consistent with theprinciples and novel features disclosed herein.

The various examples illustrated and described are provided merely asexamples to illustrate various features of the claims. However, featuresshown and described with respect to any given example are notnecessarily limited to the associated example and may be used orcombined with other examples that are shown and described. Further, theclaims are not intended to be limited by any one example.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the steps of various examples must be performed in theorder presented. As will be appreciated by one of skill in the art theorder of steps in the foregoing examples may be performed in any order.Words such as “thereafter,” “then,” “next,” etc. are not intended tolimit the order of the steps; these words are simply used to guide thereader through the description of the methods. Further, any reference toclaim elements in the singular, for example, using the articles “a,”“an” or “the” is not to be construed as limiting the element to thesingular.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the examples disclosedherein may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentdisclosure.

The hardware used to implement the various illustrative logics, logicalblocks, modules, and circuits described in connection with the examplesdisclosed herein may be implemented or performed with a general purposeprocessor, a DSP, an ASIC, an FPGA or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general-purpose processor may be a microprocessor, but, in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Alternatively, some steps or methods may be performed bycircuitry that is specific to a given function.

In some exemplary examples, the functions described may be implementedin hardware, software, firmware, or any combination thereof. Ifimplemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable storagemedium or non-transitory processor-readable storage medium. The steps ofa method or algorithm disclosed herein may be embodied in aprocessor-executable software module which may reside on anon-transitory computer-readable or processor-readable storage medium.Non-transitory computer-readable or processor-readable storage media maybe any storage media that may be accessed by a computer or a processor.By way of example but not limitation, such non-transitorycomputer-readable or processor-readable storage media may include RAM,ROM, EEPROM, FLASH memory, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othermedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computer.Disk and disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk, and blu-raydisc where disks usually reproduce data magnetically, while discsreproduce data optically with lasers. Combinations of the above are alsoincluded within the scope of non-transitory computer-readable andprocessor-readable media. Additionally, the operations of a method oralgorithm may reside as one or any combination or set of codes and/orinstructions on a non-transitory processor-readable storage mediumand/or computer-readable storage medium, which may be incorporated intoa computer program product.

The preceding description of the disclosed examples is provided toenable any person skilled in the art to make or use the presentdisclosure. Various modifications to these examples will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to some examples without departing from the spiritor scope of the disclosure. Thus, the present disclosure is not intendedto be limited to the examples shown herein but is to be accorded thewidest scope consistent with the following claims and the principles andnovel features disclosed herein.

What is claimed is:
 1. A system, comprising; a processing circuitconfigured to: receive signals corresponding ultrasound data; extract ablood flow waveform from the signals, the blood flow waveformcorresponds to a single pulse of the signals; determine a curvaturecharacteristic of the blood flow waveform based on a plurality of localcurvature parameters, each of the plurality of local curvatureparameters indicating a degree to which the blood flow waveform deviatesfrom a straight line at a location on the blood flow waveform; andidentify a medical condition for the blood flow waveform using the bloodflow waveform.
 2. The system of claim 1, further comprising anultrasound device configured to output the signals corresponding to theultrasound data, wherein the signals corresponding to the ultrasounddata is received by the processing circuit from the ultrasound device.3. The system of claim 2, wherein the processing circuit is furtherconfigured to determine morphological attributes of the blood flowwaveform; and determine whether misalignment of the ultrasound devicewith respect to a subject has occurred based on the morphologicalattributes.
 4. The system of claim 3, wherein determine whether themisalignment of the ultrasound device with respect to a subject hasoccurred based on the morphological attributes comprises using aclassifier to classify the morphological attributes as one of aplurality of categories, the one of the plurality of categoriescorresponds to a misalignment issue type.
 5. The system of claim 1,wherein the processing circuit is configured to extract the blood flowwaveform by: dividing the continuous signals into a plurality of bloodflow waveforms, each of which corresponds to a pulse of a subject; andderiving the blood flow waveform from the plurality of blood flowwaveforms.
 6. The system of claim 1, wherein the curvaturecharacteristic is a single curvature metric determined by summing theplurality of local curvature parameters.
 7. The system of claim 1,wherein each of the plurality of local curvature parameters isdetermined based on a second derivative of the blood flow waveform atthe location.
 8. The system of claim 1, wherein each the plurality oflocal curvature parameters is above a beat canopy threshold of the bloodflow waveform; and the beat canopy threshold comprises a percentage of atotal minimum-maximum range of the blood flow waveform.
 9. The system ofclaim 8, wherein the total minimum-maximum range of the blood flowwaveform corresponds to a diastolic-systolic range of the blood flowwaveform.
 10. The system of claim 1, wherein identifying the medicalcondition comprises: selecting one of a plurality of curvature templatesfrom a database, the one of the plurality of curvature templatescorresponds to the medical condition; and comparing the curvaturecharacteristic with the selected one of the plurality of curvaturetemplates.
 11. The system of claim 10, wherein the one of the curvaturetemplates is selected using at least one attribute of a subject.
 12. Thesystem of claim 10, wherein identifying the medical condition for theblood flow waveform comprises determine a likelihood of the medicalcondition based on comparing the curvature characteristic with theselected one of the plurality of curvature templates.
 13. The system ofclaim 10, wherein the medical condition associated with the selected oneof the plurality of curvature templates comprises large vesselocclusion.
 14. The system of claim 1, wherein identifying the medicalcondition comprises using a classifier to classify the curvaturecharacteristic as one of a plurality of categories, the one of theplurality of categories corresponds to the medical condition.
 15. Thesystem of claim 14, wherein the plurality of categories are determinedusing learning examples, each of the learning examples comprisescurvature and locations of the curvature as correlated with a medicalcondition.
 16. The system of claim 14, wherein the classifier comprisesa support vector machine (SVM) or a spectral regression kerneldiscriminant analysis (SR-KDA).
 17. the system of claim 1, furthercomprising sending a command to a medical device to automaticallyadminister a drug to treat the medical condition in response toidentifying the medical condition.
 18. The system of claim 1, furthercomprising the display device, wherein display device is configured todisplay one or more morphology indicators representing the curvaturecharacteristic of the blood flow waveform.
 19. A method, comprising:receiving signals corresponding ultrasound data; extracting a blood flowwaveform from the signals, the blood flow waveform corresponds to asingle pulse of the signals; determining a curvature characteristic ofthe blood flow waveform based on a plurality of local curvatureparameters, each of the plurality of local curvature parametersindicating a degree to which the blood flow waveform deviates from astraight line at a location on the blood flow waveform; and identifyinga medical condition for the blood flow waveform using the blood flowwaveform.
 20. A non-transitory processor-readable medium storingprocessor-readable instructions such that, when executed, causes aprocessor to: receive signals corresponding ultrasound data; extract ablood flow waveform from the signals, the blood flow waveformcorresponds to a single pulse of the signals; determine a curvaturecharacteristic of the blood flow waveform based on a plurality of localcurvature parameters, each of the plurality of local curvatureparameters indicating a degree to which the blood flow waveform deviatesfrom a straight line at a location on the blood flow waveform; andidentify a medical condition for the blood flow waveform using the bloodflow waveform.